A system for processing hyperspectral imagery: application to detecting forest speciesстатья
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Дата последнего поиска статьи во внешних источниках: 10 ноября 2014 г.
Аннотация:Although there are many approaches to hyperspectral data processing, they are typically
based on an intuitive search of the appropriate spectral channels to solve the
pattern recognition problem. To account for the accuracy of the computational procedures
used, optimization techniques are needed to select the most useful spectral
channels and to find contextual links for neighbouring pixels within a particular
class of observed objects. We describe a system that merges both these types of
mathematical formalism using a step-up method to extract the optimal channels from
their entire set and to explain the contextual constraints on the images of high spatial
resolution. The method is applied to forests of different species and age, which include
areas illuminated by the Sun and shaded areas; these are the main classes recognized in
this study. The proposed improvements in finding the specific information layers serve
to enhance the computational efficiency of the techniques applied. These layers are
formed by the sunlit forest canopy, sunlit background, and shaded background for a
particular solar zenith angle during an aerial survey. The original system is created
based on the relevant construction of the classifier employed, bearing in mind the
signal to noise ratio of the hyperspectral device, its calibration, and the elaborated
procedures of imagery processing. Results are shown of the related applications using
the proposed system, which reveal the higher diversity in mapping forest classes due to
the separation of the pixels in accordance with the indicated information layers. The
accuracy of the pattern recognition for the processed scenes is shown to increase as the
listed procedures are realized.